DBMS > AgensGraph vs. EsgynDB vs. MonetDB vs. XTDB

System Properties Comparison AgensGraph vs. EsgynDB vs. MonetDB vs. XTDB

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Editorial information provided by DB-Engines

Name

Description

Multi-model database supporting relational and graph data models and built upon PostgreSQL

Enterprise-class SQL-on-Hadoop solution, powered by Apache Trafodion

A relational database management system that stores data in columns

A general purpose database with bitemporal SQL and Datalog and graph queries

Primary database model

Graph DBMS
Relational DBMS

Relational DBMS

Document store

Secondary database models

Document store
Spatial DBMS

DB-Engines Ranking measures the popularity of database management systems
Trend Chart

Score	0.20
Rank	#321	Overall
	#27	Graph DBMS
	#143	Relational DBMS

Score	0.16
Rank	#329	Overall
	#146	Relational DBMS

Score	1.72
Rank	#145	Overall
	#67	Relational DBMS

Score	0.11
Rank	#343	Overall
	#46	Document stores

Website

bitnine.net/agensgraph

www.esgyn.cn

www.monetdb.org

github.com/xtdb/xtdb
www.xtdb.com

Technical documentation

bitnine.net/documentation

www.monetdb.org/Documentation

www.xtdb.com/docs

Developer

Bitnine Global Inc.

Esgyn

MonetDB BV

Juxt Ltd.

Initial release

2016

2015

2004

2019

Current release

2.1, December 2018

Dec2023 (11.49), December 2023

1.19, September 2021

License

Commercial or Open Source

Open Source

Apache License 2.0

commercial

Open Source

Mozilla Public License 2.0

Open Source

MIT License

Cloud-based only

Only available as a cloud service

DBaaS offerings (sponsored links)

Database as a Service

Providers of DBaaS offerings, please contact us to be listed.

Implementation language

C++, Java

Clojure

Server operating systems

Linux
OS X
Windows

Linux

FreeBSD
Linux
OS X
Solaris
Windows

All OS with a Java 8 (and higher) VM
Linux

Data scheme

depending on used data model

yes

schema-free

Typing

predefined data types such as float or date

yes

yes, extensible-data-notation format

XML support

Some form of processing data in XML format, e.g. support for XML data structures, and/or support for XPath, XQuery or XSLT.

Secondary indexes

yes

SQL

Support of SQL

yes

SQL 2003 with some extensions

limited SQL, making use of Apache Calcite

APIs and other access methods

Cypher Query Language
JDBC

ADO.NET
JDBC
ODBC

JDBC
native C library

MAPI library (MonetDB application programming interface)
ODBC

HTTP REST
JDBC

Supported programming languages

C
Java
JavaScript
Python

All languages supporting JDBC/ODBC/ADO.Net

C
C++
Java
JavaScript (Node.js)
Perl
PHP
Python
R
Ruby

Clojure
Java

Server-side scripts

Stored procedures

yes

Java Stored Procedures

yes, in SQL, C, R

Triggers

yes

Partitioning methods

Methods for storing different data on different nodes

no, but can be realized using table inheritance

Sharding

Sharding via remote tables

none

Replication methods

Methods for redundantly storing data on multiple nodes

Source-replica replication

Multi-source replication between multi datacenters

none

Source-replica replication available in experimental status

yes, each node contains all data

MapReduce

Offers an API for user-defined Map/Reduce methods

yes

Consistency concepts

Methods to ensure consistency in a distributed system

Immediate Consistency

Foreign keys

Referential integrity

yes

Transaction concepts

Support to ensure data integrity after non-atomic manipulations of data

ACID

Concurrency

Support for concurrent manipulation of data

yes

Durability

Support for making data persistent

yes

yes, flexibel persistency by using storage technologies like Apache Kafka, RocksDB or LMDB

In-memory capabilities

Is there an option to define some or all structures to be held in-memory only.

User concepts

Access control

fine grained access rights according to SQL-standard

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